In our previous work, we developed four major bioinformatics tools/databases: (1) the web-based ANOVA-FDR software to provide user-friendly microarray data analysis (http://lgsun.grc.nia.nih.gov/ANOVA/); (2) an algorithm and a fully-automated computational pipeline for transcript assembly from expressed sequences aligned to the mouse genome; (3) a web-based browser to visualize all transcripts and alternative spliced forms of mouse genes (NIA Mouse Gene Index: http://lgsun.grc.nia.nih.gov/geneindex/mm9/); (4) a web-based database and tool to visualize and map the transcription factor binding sites of the mouse genome (CisView: http://lgsun.grc.nia.nih.gov/geneindex/mm6/cisview.html); and (5) a web-based tool to identify consensus sequence motifs based on the genome-wide chromatin-immunoprecipitation coupled with sequencing (ChIP-Seq) method (CisFinder: http: http://lgsun.grc.nia.nih.gov/CisFinder/). We have also developed a new algorithm to simulate the gene expression regulated by two competing transcription factors. Utilizing the bioinformatics tools developed here, we analyzed the global gene expression profiles generated after the overexpression of specific transcriptions factors in mouse ES cells. The analyses helped to identify cis-regulatory elements for both active (i.e., bound by P300, CHD7, mediator, cohesin, and SWI/SNF) or repressed (i.e., with H3K27me3 histone marks and bound by Polycomb factors) states. These results were also used to identify most likely downstream target genes for specific transcription factors. We continued to apply our software tools to various biological problems of our own and in collaboration, which yielded a few publications. We are working constantly to maintain and update the bioinformatics resources that we have developed and made available to the research community. One more recent example is the development of a web-based software tool that allows users to carry out comprehensive analyses of gene expression profiling data by uploading their own data or downloading data from the public database, such as GEO. This webtool has been made freely available to the research community. In the past year, we have also developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression proles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (xed eects, random eects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression prole; (6) gene specicity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and proteinprotein interaction) are preloaded and can be used for functional annotations.In our previous work, we developed four major bioinformatics tools/databases: (1) the web-based ANOVA-FDR software to provide user-friendly microarray data analysis (http://lgsun.grc.nia.nih.gov/ANOVA/); (2) an algorithm and a fully-automated computational pipeline for transcript assembly from expressed sequences aligned to the mouse genome; (3) a web-based browser to visualize all transcripts and alternative spliced forms of mouse genes (NIA Mouse Gene Index: http://lgsun.grc.nia.nih.gov/geneindex/mm9/); (4) a web-based database and tool to visualize and map the transcription factor binding sites of the mouse genome (CisView: http://lgsun.grc.nia.nih.gov/geneindex/mm6/cisview.html); and (5) a web-based tool to identify consensus sequence motifs based on the genome-wide chromatin-immunoprecipitation coupled with sequencing (ChIP-Seq) method (CisFinder: http: http://lgsun.grc.nia.nih.gov/CisFinder/). We have also developed a new algorithm to simulate the gene expression regulated by two competing transcription factors. Utilizing the bioinformatics tools developed here, we analyzed the global gene expression profiles generated after the overexpression of specific transcriptions factors in mouse ES cells. The analyses helped to identify cis-regulatory elements for both active (i.e., bound by P300, CHD7, mediator, cohesin, and SWI/SNF) or repressed (i.e., with H3K27me3 histone marks and bound by Polycomb factors) states. These results were also used to identify most likely downstream target genes for specific transcription factors. We continued to apply our software tools to various biological problems of our own and in collaboration, which yielded a few publications. We are working constantly to maintain and update the bioinformatics resources that we have developed and made available to the research community. One more recent example is the development of a web-based software tool that allows users to carry out comprehensive analyses of gene expression profiling data by uploading their own data or downloading data from the public database, such as GEO. This webtool has been made freely available to the research community. In the past year, we have also developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression proles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (xed eects, random eects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression prole; (6) gene specicity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and proteinprotein interaction) are preloaded and can be used for functional annotations.